{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "city='Nanjing'\n",
    "city_cn='南京'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in Nanjing 南京程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=\"select * from _201903v2 where monthly_salary>0 and monthly_salary<80000 and YEAR(publish_date)=2019 and MONTH(publish_date)=3 and city='{}'\".format(city), con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']\n",
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年南京程序员的平均工资为12958元，工资中位数为12500元，其中95%的人的工资位于5000到27500元之间。\n"
     ]
    }
   ],
   "source": [
    "salary_mean=data.monthly_salary.mean()\n",
    "salary_median=data.monthly_salary.median()\n",
    "q=np.quantile(data.monthly_salary,[0.025,0.975])\n",
    "print('2019年{}程序员的平均工资为{:.0f}元，工资中位数为{:.0f}元，其中95%的人的工资位于{:.0f}到{:.0f}元之间。'\n",
    "      .format(city_cn,salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2019, Developers in Nanjing earn 12958 Yuan as average, the median is 12500 Yuan, 95% of them earn between 5000 and 27500 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print('In 2019, Developers in {} earn {:.0f} Yuan as average, '\n",
    "      'the median is {:.0f} Yuan, 95% of them earn between {:.0f} and {:.0f} Yuan.'\n",
    "      .format(city,salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x204ffc28f60>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=694.7223514765786, pvalue=1.3898186955950746e-151)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(0.5, 20), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    count=[]\n",
    "    for feature in features:\n",
    "        salary_mean.append(data[data[feature]==1].monthly_salary.mean())\n",
    "        salary_median.append(data[data[feature]==1].monthly_salary.median())\n",
    "        count.append(data[data[feature]==1].shape[0])\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_category_column(data, prefix):\n",
    "    prefix_=prefix+\"_\"\n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    data.loc[:,prefix]=['']*data.shape[0]\n",
    "    for feature in features:\n",
    "        data.loc[data[feature]==1,prefix]=feature.replace(prefix_,'')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_90da92f8_69d1_11e9_afb2_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >career_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2048025eba8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career=get_sub_stats(data,'career_')\n",
    "data_career.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_91a258fa_69d1_11e9_b00f_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >9</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow0_col0\" class=\"data row0 col0\" >lua</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow0_col1\" class=\"data row0 col1\" >20735.3</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow0_col2\" class=\"data row0 col2\" >15000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow0_col3\" class=\"data row0 col3\" >17</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.59%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >16</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow1_col0\" class=\"data row1 col0\" >rust</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow1_col1\" class=\"data row1 col1\" >18750</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow1_col2\" class=\"data row1 col2\" >18750</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow1_col3\" class=\"data row1 col3\" >2</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow1_col4\" class=\"data row1 col4\" >0.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >15</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow2_col0\" class=\"data row2 col0\" >ruby</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow2_col1\" class=\"data row2 col1\" >18625</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow2_col2\" class=\"data row2 col2\" >16000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow2_col3\" class=\"data row2 col3\" >12</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow2_col4\" class=\"data row2 col4\" >0.41%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >4</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow3_col0\" class=\"data row3 col0\" >haskell</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow3_col1\" class=\"data row3 col1\" >17500</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow3_col2\" class=\"data row3 col2\" >17500</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow3_col3\" class=\"data row3 col3\" >1</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow3_col4\" class=\"data row3 col4\" >0.03%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >12</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow4_col0\" class=\"data row4 col0\" >perl</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow4_col1\" class=\"data row4 col1\" >17366.7</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow4_col3\" class=\"data row4 col3\" >20</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow4_col4\" class=\"data row4 col4\" >0.69%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >8</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow5_col0\" class=\"data row5 col0\" >kotlin</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow5_col1\" class=\"data row5 col1\" >16375</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow5_col2\" class=\"data row5 col2\" >13250</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow5_col3\" class=\"data row5 col3\" >4</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow5_col4\" class=\"data row5 col4\" >0.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >14</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow6_col0\" class=\"data row6 col0\" >python</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow6_col1\" class=\"data row6 col1\" >15279.5</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow6_col2\" class=\"data row6 col2\" >14583.3</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow6_col3\" class=\"data row6 col3\" >244</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow6_col4\" class=\"data row6 col4\" >8.44%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >3</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow7_col0\" class=\"data row7 col0\" >go</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow7_col1\" class=\"data row7 col1\" >15211.7</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow7_col2\" class=\"data row7 col2\" >14000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow7_col3\" class=\"data row7 col3\" >160</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow7_col4\" class=\"data row7 col4\" >5.53%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >10</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow8_col0\" class=\"data row8 col0\" >matlab</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow8_col1\" class=\"data row8 col1\" >14689</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow8_col2\" class=\"data row8 col2\" >14791.7</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow8_col3\" class=\"data row8 col3\" >56</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow8_col4\" class=\"data row8 col4\" >1.94%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >1</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow9_col0\" class=\"data row9 col0\" >cpp</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow9_col1\" class=\"data row9 col1\" >14184.9</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow9_col2\" class=\"data row9 col2\" >13000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow9_col3\" class=\"data row9 col3\" >553</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow9_col4\" class=\"data row9 col4\" >19.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >5</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow10_col0\" class=\"data row10 col0\" >java</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow10_col1\" class=\"data row10 col1\" >13137.6</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow10_col2\" class=\"data row10 col2\" >12500</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow10_col3\" class=\"data row10 col3\" >1014</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow10_col4\" class=\"data row10 col4\" >35.06%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row11\" class=\"row_heading level0 row11\" >18</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow11_col0\" class=\"data row11 col0\" >swift</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow11_col1\" class=\"data row11 col1\" >12490.7</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow11_col2\" class=\"data row11 col2\" >11750</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow11_col3\" class=\"data row11 col3\" >18</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow11_col4\" class=\"data row11 col4\" >0.62%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row12\" class=\"row_heading level0 row12\" >0</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow12_col0\" class=\"data row12 col0\" >c_sharp</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow12_col1\" class=\"data row12 col1\" >11830.3</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow12_col2\" class=\"data row12 col2\" >11000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow12_col3\" class=\"data row12 col3\" >310</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow12_col4\" class=\"data row12 col4\" >10.72%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row13\" class=\"row_heading level0 row13\" >6</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow13_col0\" class=\"data row13 col0\" >javascript</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow13_col1\" class=\"data row13 col1\" >11663</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow13_col2\" class=\"data row13 col2\" >11500</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow13_col3\" class=\"data row13 col3\" >386</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow13_col4\" class=\"data row13 col4\" >13.35%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row14\" class=\"row_heading level0 row14\" >11</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow14_col0\" class=\"data row14 col0\" >objective_c</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow14_col1\" class=\"data row14 col1\" >11250</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow14_col2\" class=\"data row14 col2\" >12000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow14_col3\" class=\"data row14 col3\" >6</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow14_col4\" class=\"data row14 col4\" >0.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row15\" class=\"row_heading level0 row15\" >13</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow15_col0\" class=\"data row15 col0\" >php</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow15_col1\" class=\"data row15 col1\" >11000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow15_col2\" class=\"data row15 col2\" >9000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow15_col3\" class=\"data row15 col3\" >79</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow15_col4\" class=\"data row15 col4\" >2.73%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row16\" class=\"row_heading level0 row16\" >19</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow16_col0\" class=\"data row16 col0\" >typescript</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow16_col1\" class=\"data row16 col1\" >10875</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow16_col2\" class=\"data row16 col2\" >12000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow16_col3\" class=\"data row16 col3\" >4</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow16_col4\" class=\"data row16 col4\" >0.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row17\" class=\"row_heading level0 row17\" >2</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow17_col0\" class=\"data row17 col0\" >delphi</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow17_col1\" class=\"data row17 col1\" >8958.33</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow17_col2\" class=\"data row17 col2\" >10000</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow17_col3\" class=\"data row17 col3\" >6</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow17_col4\" class=\"data row17 col4\" >0.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row18\" class=\"row_heading level0 row18\" >7</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow18_col0\" class=\"data row18 col0\" >julia</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow18_col1\" class=\"data row18 col1\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow18_col2\" class=\"data row18 col2\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow18_col3\" class=\"data row18 col3\" >0</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow18_col4\" class=\"data row18 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row19\" class=\"row_heading level0 row19\" >17</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow19_col0\" class=\"data row19 col0\" >scrala</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow19_col1\" class=\"data row19 col1\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow19_col2\" class=\"data row19 col2\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow19_col3\" class=\"data row19 col3\" >0</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow19_col4\" class=\"data row19 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row20\" class=\"row_heading level0 row20\" >20</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow20_col0\" class=\"data row20 col0\" >vba</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow20_col1\" class=\"data row20 col1\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow20_col2\" class=\"data row20 col2\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow20_col3\" class=\"data row20 col3\" >0</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow20_col4\" class=\"data row20 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_91a258fa_69d1_11e9_b00f_701ce71031eflevel0_row21\" class=\"row_heading level0 row21\" >21</th> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow21_col0\" class=\"data row21 col0\" >visual_basic</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow21_col1\" class=\"data row21 col1\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow21_col2\" class=\"data row21 col2\" >nan</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow21_col3\" class=\"data row21 col3\" >0</td> \n",
       "        <td id=\"T_91a258fa_69d1_11e9_b00f_701ce71031efrow21_col4\" class=\"data row21 col4\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2048025ee48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats(data,'pl_')\n",
    "data_pl.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_078ea9de_584a_11e9_b760_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >edu_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_078ea9de_584a_11e9_b760_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >5</th> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow0_col0\" class=\"data row0 col0\" >phd</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow0_col1\" class=\"data row0 col1\" >34305.6</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow0_col2\" class=\"data row0 col2\" >37500</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow0_col3\" class=\"data row0 col3\" >9</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.15%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_078ea9de_584a_11e9_b760_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow1_col0\" class=\"data row1 col0\" >master</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow1_col1\" class=\"data row1 col1\" >17301.2</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow1_col2\" class=\"data row1 col2\" >17500</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow1_col3\" class=\"data row1 col3\" >267</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow1_col4\" class=\"data row1 col4\" >4.44%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_078ea9de_584a_11e9_b760_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >1</th> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow2_col0\" class=\"data row2 col0\" >bachelor</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow2_col1\" class=\"data row2 col1\" >13548.1</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow2_col3\" class=\"data row2 col3\" >4050</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow2_col4\" class=\"data row2 col4\" >67.39%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_078ea9de_584a_11e9_b760_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >0</th> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow3_col0\" class=\"data row3 col0\" >associate</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow3_col1\" class=\"data row3 col1\" >10455.9</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow3_col2\" class=\"data row3 col2\" >10000</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow3_col3\" class=\"data row3 col3\" >1678</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow3_col4\" class=\"data row3 col4\" >27.92%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_078ea9de_584a_11e9_b760_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >2</th> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow4_col0\" class=\"data row4 col0\" >high_school</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow4_col1\" class=\"data row4 col1\" >9350</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow4_col3\" class=\"data row4 col3\" >5</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow4_col4\" class=\"data row4 col4\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_078ea9de_584a_11e9_b760_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >4</th> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow5_col0\" class=\"data row5 col0\" >middle_school</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow5_col1\" class=\"data row5 col1\" >9000</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow5_col2\" class=\"data row5 col2\" >9000</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow5_col3\" class=\"data row5 col3\" >1</td> \n",
       "        <td id=\"T_078ea9de_584a_11e9_b760_701ce71031efrow5_col4\" class=\"data row5 col4\" >0.02%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2376dc2b5c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats(data,'edu_')\n",
    "data_edu.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_phd=data[data.edu_phd==1].monthly_salary * 10000\n",
    "salary_master=data[data.edu_master==1].monthly_salary * 10000\n",
    "salary_bachelor=data[data.edu_bachelor==1].monthly_salary * 10000\n",
    "salary_associate=data[data.edu_associate==1].monthly_salary * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是10456，13548，17301，34306。中位数分别是10000， 12500，17500， 37500。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=38.585023359430714, pvalue=1.9390733172835053e-09)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=95.60033356433718, pvalue=2.3949217533503604e-22)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=363.36224927786384, pvalue=1.3590274262634433e-78)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'edu')\n",
    "order=['phd','master','bachelor','associate']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_158b6b86_584a_11e9_aa85_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >experience_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_158b6b86_584a_11e9_aa85_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow0_col0\" class=\"data row0 col0\" >10</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow0_col1\" class=\"data row0 col1\" >27460.8</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow0_col2\" class=\"data row0 col2\" >25000</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow0_col3\" class=\"data row0 col3\" >17</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.24%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_158b6b86_584a_11e9_aa85_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow1_col1\" class=\"data row1 col1\" >19196.5</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow1_col2\" class=\"data row1 col2\" >17500</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow1_col3\" class=\"data row1 col3\" >670</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow1_col4\" class=\"data row1 col4\" >9.45%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_158b6b86_584a_11e9_aa85_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow2_col1\" class=\"data row2 col1\" >14159.6</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow2_col3\" class=\"data row2 col3\" >1977</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow2_col4\" class=\"data row2 col4\" >27.88%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_158b6b86_584a_11e9_aa85_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >4</th> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow3_col0\" class=\"data row3 col0\" >no</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow3_col1\" class=\"data row3 col1\" >11584.7</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow3_col2\" class=\"data row3 col2\" >10833.3</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow3_col3\" class=\"data row3 col3\" >1949</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow3_col4\" class=\"data row3 col4\" >27.49%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_158b6b86_584a_11e9_aa85_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >1</th> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow4_col1\" class=\"data row4 col1\" >11053.5</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow4_col2\" class=\"data row4 col2\" >10416.7</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow4_col3\" class=\"data row4 col3\" >2478</td> \n",
       "        <td id=\"T_158b6b86_584a_11e9_aa85_701ce71031efrow4_col4\" class=\"data row4 col4\" >34.95%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2376dc37278>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats(data,'experience_')\n",
    "data_experience.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience_10==1].monthly_salary * 10000\n",
    "salary_we_5_10=data[data.experience_5_10==1].monthly_salary * 10000\n",
    "salary_we_3_5=data[data.experience_3_5==1].monthly_salary * 10000\n",
    "salary_we_1_3=data[data.experience_1_3==1].monthly_salary * 10000\n",
    "salary_we_no=data[data.experience_no==1].monthly_salary * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'experience')\n",
    "order=['10','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_type_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >9</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow0_col0\" class=\"data row0 col0\" >us_eu</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow0_col1\" class=\"data row0 col1\" >15029</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow0_col2\" class=\"data row0 col2\" >14000</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow0_col3\" class=\"data row0 col3\" >218</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow0_col4\" class=\"data row0 col4\" >3.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >6</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow1_col0\" class=\"data row1 col0\" >public_institution</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow1_col1\" class=\"data row1 col1\" >14367</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow1_col2\" class=\"data row1 col2\" >13000</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow1_col3\" class=\"data row1 col3\" >61</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow1_col4\" class=\"data row1 col4\" >0.91%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >8</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow2_col0\" class=\"data row2 col0\" >state</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow2_col1\" class=\"data row2 col1\" >13393</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow2_col3\" class=\"data row2 col3\" >475</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow2_col4\" class=\"data row2 col4\" >7.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow3_col0\" class=\"data row3 col0\" >listed</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow3_col1\" class=\"data row3 col1\" >13321</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow3_col2\" class=\"data row3 col2\" >12500</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow3_col3\" class=\"data row3 col3\" >622</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow3_col4\" class=\"data row3 col4\" >9.27%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >7</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow4_col0\" class=\"data row4 col0\" >startup</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow4_col1\" class=\"data row4 col1\" >13113</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow4_col3\" class=\"data row4 col3\" >182</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow4_col4\" class=\"data row4 col4\" >2.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow5_col0\" class=\"data row5 col0\" >private</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow5_col1\" class=\"data row5 col1\" >12634</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow5_col2\" class=\"data row5 col2\" >12000</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow5_col3\" class=\"data row5 col3\" >4999</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow5_col4\" class=\"data row5 col4\" >74.52%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >0</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow6_col0\" class=\"data row6 col0\" >foreign</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow6_col1\" class=\"data row6 col1\" >11629</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow6_col2\" class=\"data row6 col2\" >12500</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow6_col3\" class=\"data row6 col3\" >148</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow6_col4\" class=\"data row6 col4\" >2.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >2</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow7_col0\" class=\"data row7 col0\" >foreign_rep</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow7_col1\" class=\"data row7 col1\" >6000</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow7_col2\" class=\"data row7 col2\" >2500</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow7_col3\" class=\"data row7 col3\" >3</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow7_col4\" class=\"data row7 col4\" >0.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >1</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow8_col0\" class=\"data row8 col0\" >foreign_gov</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow8_col1\" class=\"data row8 col1\" >nan</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow8_col2\" class=\"data row8 col2\" >nan</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow8_col3\" class=\"data row8 col3\" >0</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow8_col4\" class=\"data row8 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >4</th> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow9_col0\" class=\"data row9 col0\" >non_profit</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow9_col1\" class=\"data row9 col1\" >nan</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow9_col2\" class=\"data row9 col2\" >nan</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow9_col3\" class=\"data row9 col3\" >0</td> \n",
       "        <td id=\"T_1e1d0d10_584a_11e9_bd5b_701ce71031efrow9_col4\" class=\"data row9 col4\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2376dd50d68>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats(data,'company_type_')\n",
    "data_company_type.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_1f34c51c_584a_11e9_90ca_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_size_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_1f34c51c_584a_11e9_90ca_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow0_col1\" class=\"data row0 col1\" >17824</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow0_col2\" class=\"data row0 col2\" >15000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow0_col3\" class=\"data row0 col3\" >209</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow0_col4\" class=\"data row0 col4\" >2.96%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1f34c51c_584a_11e9_90ca_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >4</th> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow1_col0\" class=\"data row1 col0\" >5000_10000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow1_col1\" class=\"data row1 col1\" >14656</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow1_col2\" class=\"data row1 col2\" >14000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow1_col3\" class=\"data row1 col3\" >131</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow1_col4\" class=\"data row1 col4\" >1.85%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1f34c51c_584a_11e9_90ca_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >1</th> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow2_col0\" class=\"data row2 col0\" >1000_5000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow2_col1\" class=\"data row2 col1\" >14212</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow2_col3\" class=\"data row2 col3\" >818</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow2_col4\" class=\"data row2 col4\" >11.58%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1f34c51c_584a_11e9_90ca_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >5</th> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow3_col0\" class=\"data row3 col0\" >500_1000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow3_col1\" class=\"data row3 col1\" >13797</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow3_col2\" class=\"data row3 col2\" >12500</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow3_col3\" class=\"data row3 col3\" >673</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow3_col4\" class=\"data row3 col4\" >9.52%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1f34c51c_584a_11e9_90ca_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >2</th> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow4_col0\" class=\"data row4 col0\" >150_500</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow4_col1\" class=\"data row4 col1\" >12692</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow4_col2\" class=\"data row4 col2\" >12000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow4_col3\" class=\"data row4 col3\" >1665</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow4_col4\" class=\"data row4 col4\" >23.56%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1f34c51c_584a_11e9_90ca_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >6</th> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow5_col0\" class=\"data row5 col0\" >50_150</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow5_col1\" class=\"data row5 col1\" >12359</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow5_col2\" class=\"data row5 col2\" >11500</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow5_col3\" class=\"data row5 col3\" >2315</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow5_col4\" class=\"data row5 col4\" >32.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_1f34c51c_584a_11e9_90ca_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >3</th> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow6_col0\" class=\"data row6 col0\" >50</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow6_col1\" class=\"data row6 col1\" >11694</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow6_col2\" class=\"data row6 col2\" >11000</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow6_col3\" class=\"data row6 col3\" >1255</td> \n",
       "        <td id=\"T_1f34c51c_584a_11e9_90ca_701ce71031efrow6_col4\" class=\"data row6 col4\" >17.76%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2376d97d358>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats(data,'company_size_')\n",
    "data_company_size.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_20856178_584a_11e9_a31b_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >industry_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >4</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow0_col0\" class=\"data row0 col0\" >finance</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow0_col1\" class=\"data row0 col1\" >14713</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow0_col2\" class=\"data row0 col2\" >12500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow0_col3\" class=\"data row0 col3\" >168</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow0_col4\" class=\"data row0 col4\" >2.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >8</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow1_col0\" class=\"data row1 col0\" >realestate</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow1_col1\" class=\"data row1 col1\" >14316</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow1_col2\" class=\"data row1 col2\" >12500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow1_col3\" class=\"data row1 col3\" >183</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow1_col4\" class=\"data row1 col4\" >2.17%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >10</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow2_col0\" class=\"data row2 col0\" >trade</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow2_col1\" class=\"data row2 col1\" >13320</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow2_col3\" class=\"data row2 col3\" >472</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow2_col4\" class=\"data row2 col4\" >5.61%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >6</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow3_col0\" class=\"data row3 col0\" >logistic</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow3_col1\" class=\"data row3 col1\" >13242</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow3_col2\" class=\"data row3 col2\" >12500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow3_col3\" class=\"data row3 col3\" >123</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow3_col4\" class=\"data row3 col4\" >1.46%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >2</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow4_col0\" class=\"data row4 col0\" >edu</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow4_col1\" class=\"data row4 col1\" >13130</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow4_col3\" class=\"data row4 col3\" >414</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow4_col4\" class=\"data row4 col4\" >4.92%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >1</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow5_col0\" class=\"data row5 col0\" >computer</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow5_col1\" class=\"data row5 col1\" >12880</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow5_col2\" class=\"data row5 col2\" >12500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow5_col3\" class=\"data row5 col3\" >6274</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow5_col4\" class=\"data row5 col4\" >74.54%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >9</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow6_col0\" class=\"data row6 col0\" >service</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow6_col1\" class=\"data row6 col1\" >12490</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow6_col2\" class=\"data row6 col2\" >12500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow6_col3\" class=\"data row6 col3\" >24</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow6_col4\" class=\"data row6 col4\" >0.29%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >3</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow7_col0\" class=\"data row7 col0\" >energy</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow7_col1\" class=\"data row7 col1\" >12482</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow7_col2\" class=\"data row7 col2\" >11500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow7_col3\" class=\"data row7 col3\" >299</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow7_col4\" class=\"data row7 col4\" >3.55%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >5</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow8_col0\" class=\"data row8 col0\" >gov</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow8_col1\" class=\"data row8 col1\" >11667</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow8_col2\" class=\"data row8 col2\" >10000</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow8_col3\" class=\"data row8 col3\" >164</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow8_col4\" class=\"data row8 col4\" >1.95%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >7</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow9_col0\" class=\"data row9 col0\" >medical</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow9_col1\" class=\"data row9 col1\" >11586</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow9_col2\" class=\"data row9 col2\" >11500</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow9_col3\" class=\"data row9 col3\" >213</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow9_col4\" class=\"data row9 col4\" >2.53%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_20856178_584a_11e9_a31b_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >0</th> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow10_col0\" class=\"data row10 col0\" >ads</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow10_col1\" class=\"data row10 col1\" >11503</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow10_col2\" class=\"data row10 col2\" >10000</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow10_col3\" class=\"data row10 col3\" >83</td> \n",
       "        <td id=\"T_20856178_584a_11e9_a31b_701ce71031efrow10_col4\" class=\"data row10 col4\" >0.99%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2376dc0e400>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats(data,'industry_')\n",
    "data_industry.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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